• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.39-55
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• 2024

This study aimed to examine research trends related to Natural Language Processing (NLP) in science education by analyzing 37 domestic and international documents that utilized NLP techniques in the field of science education from 2011 to September 2023. In particular, the study systematically analyzed the content, focusing on the main application areas of NLP techniques in science education, the role of teachers when utilizing NLP techniques, and a comparison of domestic and international perspectives. The analysis results are as follows: Firstly, it was confirmed that NLP techniques are significantly utilized in formative assessment, automatic scoring, literature review and classification, and pattern extraction in science education. Utilizing NLP in formative assessment allows for real-time analysis of students' learning processes and comprehension, reducing the burden on teachers' lessons and providing accurate, effective feedback to students. In automatic scoring, it contributes to the rapid and precise evaluation of students' responses. In literature review and classification using NLP, it helps to effectively analyze the topics and trends of research related to science education and student reports. It also helps to set future research directions. Utilizing NLP techniques in pattern extraction allows for effective analysis of commonalities or patterns in students' thoughts and responses. Secondly, the introduction of NLP techniques in science education has expanded the role of teachers from mere transmitters of knowledge to leaders who support and facilitate students' learning, requiring teachers to continuously develop their expertise. Thirdly, as domestic research on NLP is focused on literature review and classification, it is necessary to create an environment conducive to the easy collection of text data to diversify NLP research in Korea. Based on these analysis results, the study discussed ways to utilize NLP techniques in science education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Journal of Distribution Science
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• v.17 no.11
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• pp.93-101
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• 2019

Purpose: Blockchain technology suggests ways to solve the problems in the existing industry. Among them, Cryptocurrency system, which is an element of Blockchain technology, is a very important factor for operating Blockchain. While Blockchain cryptocurrency has attracted attention, studies on cryptocurrency prices have been mainly conducted, however previous studies mainly conducted on Bitcoin prices. On the other hand, in the context of the creation and trading of various cryptocurrencies based on the Blockchain system, little research has been done on cryptocurrencies other than Bitcoin. Hence, this study attempts to find variables related to the prices of Dash, Litecoin, and Monero cryptocurrencies using machine learning techniques. We also attempt to find differences in the variables related to the prices for each cryptocurrencies and to examine machine learning techniques that can provide better performance. Research design, data, and methodology: This study performed Dash, Litecoin, and Monero price prediction analysis of cryptocurrency using Blockchain information and machine learning techniques. We employed number of transactions in Blockchain, amount of generated cryptocurrency, transaction fees, number of activity accounts in Blockchain, Block creation difficulty, block size, umber of created blocks as independent variables. This study tried to ensure the reliability of the analysis results through 10-fold cross validation. Blockchain information was hierarchically added for price prediction, and the analysis result was measured as RMSE and MAPE. Results: The analysis shows that the prices of Dash, Litecoin and Monero cryptocurrency are related to Blockchain information. Also, we found that different Blockchain information improves the analysis results for each cryptocurrency. In addition, this study found that the neural network machine learning technique provides better analysis results than support-vector machine in predicting cryptocurrency prices. Conclusion: This study concludes that the information of Blockchain should be considered for the prediction of the price of Dash, Litecoin, and Monero cryptocurrency. It also suggests that Blockchain information related to the price of cryptocurrency differs depending on the type of cryptocurrency. We suggest that future research on various types of cryptocurrencies is needed. The findings of this study can provide a theoretical basis for future cryptocurrency research in distribution management.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.63-72
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• 2009

Digital forensics refers to finding electronic evidences related to crimes. As cyber crimes are increasing daily, digital forensics for finding electronic evidences is also becoming important. At present, various aspects of digital forensics have being researched including the overall process model and analysis techniques such as network forensics, system forensics and database forensics for digital forensics. Regarding database forensics, only analysis techniques dependent on specific vendors have been suggested. And general process models and analysis techniques which can be used in various databases have not been studied. This paper proposes an integrated process model and analysis technique for database forensics. The proposed database forensics model (DFM) allows us to solve problems and analyze databases according to the situation and purpose, and to use a standard model and techniques for various database analyses. In order to test our model(DFM), we applied it to various database analyses. And we confirmed the results of our experiment that it can be applicable to acquisition in the scene as well as analysis of data relationships.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.271-278
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• 2015

We use various types of cryptographic algorithms for the protection of personal and sensitive informations in the application environments, such as an internet banking and an electronic commerce. However, recent researches were introduced that if we implement modes of operation, padding method and other cryptographic implementations in a wrong way, then the critical information can be leaked even though the underlying cryptographic algorithms are secure. Among these attacking techniques, the padding oracle attack is representative. In this paper, we analyze the possibility of padding oracle attacks of 12 kinds of padding techniques that can be applied to the CBC operation mode of a block cipher. As a result, we discovered that 3 kinds were safe padding techniques and 9 kinds were unsafe padding techniques. We propose 5 considerations when designing a safe padding techniques to have a resistance to the padding oracle attack through the analysis of three kinds of safe padding techniques.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.6
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• pp.517-525
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• 2007

As usage of UAV becomes more common, a basic requirements on the system are changing. Existent system did function embodiment by major object, but current UAV puts bigger weight to availability. Therefore, all the advanced countries in UAV technologies put great efforts in reliability analysis techniques and source collection of system, and reflect the result in design. The authors are developing a flight control system for a UAV and using the reliability analysis techniques in the process. This paper introduces basic reliability analysis techniques and results of analysis for a small UAV flight control system that is developing present. The result plans efficiency enlargement UAV development and operation process.

• Smart Structures and Systems
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• v.4 no.2
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• pp.233-245
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• 2008

For historical masonry structures existing in the Mediterranean area, structural strengthening is of primary importance due to the continuous earthquake threat that is posed on them. Proper retrofitting of historical structures involves a thorough understanding of their structural pathology, before proceeding with any intervention measures. In this paper, a methodology is presented for the evaluation of the actual state of historical masonry structures, which can provide a useful tool for the seismic response assessment before and after the retrofitting. The methodology is mainly focused on the failure and vulnerability analysis of masonry structures using the finite element method. Using this methodology the retrofitting of historical structures with innovative techniques is investigated. The innovative technique presented here involves the exploitation of Shape Memory Alloy prestressed bars. This type of intervention is proposed because it ensures increased reversibility and minimization of interventions, in comparison with conventional retrofitting methods. In this paper, a case study is investigated for the demonstration of the proposed methodologies and techniques, which comprises a masonry Byzantine church and a masonry Cistern. Prestressed SMA alloy bars are placed into the load-bearing system of the structure. The seismic response of the non-retrofitted and the retrofitted finite element models are compared in terms of seismic energy dissipation and displacements diminution.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.4
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• pp.1-10
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• 1999

Free-field ground motion during earthquake is significantly affected by the local site conditions and it is essential for the seismic design to perform the site specific ground response analysis. In this paper, the procedures of site specific ground response analysis were suggested based on the Korean seismic guideline and the review of state of the art technologies. The concept of ground response analysis was introduced, and the techniques of obtaining soil data for one dimensional equivalent linear analysis which include site investigation planning, field and laboratory testing techniques, deformational characteristics of soils at small to large strains, and site characterization techniques combining field and laboratory test results, were suggested. Finally, the case study was performed at Inchon area following the suggested procedure.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.3
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• pp.138-145
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• 2006

It is well known that the accurate calculation of the field distribution is essential for the design of electrical machines. The analytical techniques for electromagnetic field can quickly and exactly determine airgap magnetic field distribution in electrical machines. Many analytical techniques have been investigated to predict the magnetic field distribution in PM machines equipped with permanent magnets. Using the analytical technique by transfer relations, D. L. Trumper and K. R. Davey already presented the design and analysis of linear permanent-magnet machines and induction machines, respectively. Using the transfer relations (Melcher's general methodology) to describe electromagnetic phenomena, this paper deals with the analysis on the magnetic field distribution due to PM and winding current, the induced voltage and the static torque characteristics in surface-mounted slotless type permanent magnet machine. The validity of the analysis results is confirmed by finite element (FE) analysis.